We divided our fine-tuning methodology into two different steps. In the first step, we considered as the baseline the Faster R-CNN detector described in the above section, having a ResNet-50 backbone pre-trained on the COCO dataset [1]}, a large collection of images depicting complex everyday scenes of ordinary objects in their natural context, divided into 80 different categories. Since this network is a generic object detector that can distinguish between many different classes of objects, we modified the EN building block to adapt the model to our purposes. In particular, we reduced the last fully connected layers of the detector to recognize and locate object instances belonging only to a specific category, i.e., the pedestrian category. Then, we trained this modified Faster R-CNN-based network exploiting our synthetic images of viped, leaving all the model weights unfrozen during this phase so that the back-propagation algorithm can tune them.
